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Combining protein secondary structure prediction models with ensemble methods of optimal complexity

NEUROCOMPUTING 2004年第1期56卷 305-327页

作者： Guermeur, Y Pollastri, G Elisseeff, A Zelus, D Paugam-Moisy, H Baldi, P Univ Nancy 1 LORIA F-54506 Vandoeuvre Les Nancy France Univ Calif Irvine Inst Genom & Bioinformat Dept Informat & Comp Sci Irvine CA 92697 USA Max Planck Inst Biol Cybernet D-72076 Tubingen Germany CIBIO Wiener Lab RA-2000 Rosario Santa Fe Argentina Univ Lyon 2 UMR CNRS 5015 ISC F-69675 Bron France

Many sophisticated methods are currently available to perform protein secondary structure prediction. Since they are frequently based on different principles, and different knowledge sources, significant benefits can be expected from combining them. However, the choice of an appropriate combiner appears to be an issue in its own right. The first difficulty to overcome when combining prediction methods is overfitting. This is the reason why we investigate the implementation of Support Vector Machines to perform the task. A family of multi-class SVMs is introduced. Two of these machines are used to combine some of the current best protein secondary structure prediction methods. Their performance is consistently superior to the performance of the ensemble methods traditionally used in the field. They also outperform the decomposition approaches based on bi-class SVMs. Furthermore, initial experimental evidence suggests that their outputs could be processed by the biologist to perform higher-level treatments. (C) 2003 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction multi-class support vector machines (M-SVMs) ensemble methods hierarchical sequence processing systems

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A protein secondary structure prediction framework based on the Extreme Learning Machine

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NEUROCOMPUTING 2008年第1-3期72卷 262-268页

作者： Wang, Guoren Zhao, Yi Wang, Di Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

In this paper we propose an Extreme Learning Machine (ELM) based protein secondary structure prediction framework which can provide good performance at extremely fast speed. To achieve better performance, in this framework: (i) the three secondary structures are independently predicted by a binary ELM classifier first;(ii) a probability based combination (PBC) method is then proposed to combine these binary prediction results into the expected three-classification results and (iii) a helix postprocessing (HPP) method is finally proposed to further improve the overall performance of the framework based on biological features. Experiments conducted on the real data sets CB513 and RS 126 demonstrate that our algorithm can achieve as good prediction accuracy as other popular methods;however, at very fast learning speed. (C) 2008 Elsevier B.V. All rights reserved.

关键词： ELM protein secondary structure prediction Probability based combination Helix postprocessing

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IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction

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proteinS-structure FUNCTION AND BIOINFORMATICS 2021年第10期89卷 1277-1288页

作者： Gormez, Yasin Sabzekar, Mostafa Aydin, Zafer Sivas Cumhuriyet Univ Fac Econ & Adm Sci Management Informat Syst Sivas Turkey Birjand Univ Technol Dept Comp Engn Birjand Iran Abdullah Gul Univ Comp Engn Dept Engn Fac Kayseri Turkey

There is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.

关键词： Bayesian optimization convolutional neural network deep learning graph convolutional network protein secondary structure prediction

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A new hybrid coding for protein secondary structure prediction based on primary structure similarity

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GENE 2017年 618卷 8-13页

作者： Li, Zhong Wang, Jing Zhang, Shunpu Zhang, Qifeng Wu, Wuming Zhejiang Sci Tech Univ Coll Sci Hangzhou 30018 Zhejiang Peoples R China Univ Cent Florida Dept Stat Orlando FL 32816 USA

The coding pattern of protein can greatly affect the prediction accuracy of protein secondary structure. In this paper, a novel hybrid coding method based on the physicochemical properties of amino acids and tendency factors is proposed for the prediction of protein secondary structure. The principal component analysis (PCA) is first applied to the physicochemical properties of amino acids to construct a 3-bit-code, and then the 3 tendency factors of amino acids are calculated to generate another 3-bit-code. Two 3-bit-codes are fused to form a novel hybrid 6-bit-code. Furthermore, we make a geometry-based similarity comparison of the protein primary structure between the reference set and the test set before the secondary structure prediction. We finally use the support vector machine (SVM) to predict those amino acids which are not detected by the primary structure similarity comparison. Experimental results show that our method achieves a satisfactory improvement in accuracy in the prediction of protein secondary structure. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Hybrid code protein secondary structure prediction protein primary structure Support vector machine

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MFTrans: A multi-feature transformer network for protein secondary structure prediction

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2024年第Part1期267卷 131311页

作者： Chen, Yifu Chen, Guanxing Chen, Calvin Yu-Chian Sun Yat Sen Univ Artificial Intelligence Med Res Ctr Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Peking Univ Shenzhen Grad Sch AI Sci Preferred Program AI4S Shenzhen 518055 Guangdong Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen 518055 Guangdong Peoples R China China Med Univ Hosp Dept Med Res Taichung 40447 Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung 41354 Taiwan

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning -based multi -feature fusion network aimed at enhancing the precision and efficiency of protein secondary structure prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi -view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi -feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

关键词： protein secondary structure prediction Multiple sequence alignment Feature fusion Deep learning

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Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology

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JOURNAL OF SUPERCOMPUTING 2007年第1期41卷 1-16页

作者： Zhong, Wei Altun, Gulsah Tian, Xinmin Harrison, Robert Tai, Phang C. Pan, Yi Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ S Carolina Upstate Div Math & Comp Sci Spartanburg SC 29309 USA Intel Corp Software Solut Grp Santa Clara CA USA Georgia State Univ Dept Biol Atlanta GA 30303 USA

protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, tertiary classifiers for the protein secondary structure prediction are implemented on Denoeux Belief Neural Network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 matrix and PSSM matrix are experimented separately as the encoding schemes for DBNN. Hydrophobicity matrix, BLOSUM62 matrix and PSSM matrix are applied to DBNN architecture for the first time. The experimental results contribute to the design of new encoding schemes. Our accuracy of the tertiary classifier with PSSM encoding scheme reaches 72.01%, which is almost 10% better than the previous results obtained in 2003. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the Hyper-Threading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that Hyper-Threading technology for Intel architecture is efficient for parallel biological algorithms.

关键词： neural networks protein secondary structure prediction parallel architecture speedup DBNN (Denoeux Belief Neural Network) MPI (Message Passing Interface) OpenMP Pthread hyper-threading BLOSUM62 matrix hydrophobicity matrix PSSM (Position Specific Scoring Matrix)

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HYBRID SYSTEM FOR protein secondary structure prediction

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JOURNAL OF MOLECULAR BIOLOGY 1992年第4期225卷 1049-1063页

作者： ZHANG, X MESIROV, JP WALTZ, DL Thinking Machines Corporation Cambridge MA 02142.

We have developed a hybrid system to predict the secondary structures (alpha-helix, beta-sheet and coil) of proteins and achieved 66.4% accuracy, with correlation coefficients of C(coil) = 0.429, C alpha = 0.470 and C beta = 0.387. This system contains three subsystems ("experts"): a neural network module, a statistical module and a memory-based reasoning module. First, the three experts independently learn the mapping between amino acid sequences and secondary structures from the known protein structures, then a Combiner learns to combine automatically the outputs of the experts to make final predictions. The hybrid system was tested with 107 protein structures through k-way cross-validation. Its performance was better than each expert and all previously reported methods with greater than 0.99 statistical significance. It was observed that for 20% of the residues, all three experts produced the same but wrong predictions. This may suggest an upper bound on the accuracy of secondary structure predictions based on local information from the currently available protein structures, and indicate places where non-local interactions may play a dominant role in conformation. For 64% of the residues, at least two experts were the same and correct, which shows that the Combiner performed better than majority vote. For 77% of the residues, at least one expert was correct, thus there may still be room for improvement in this hybrid approach. Rigorous evaluation procedures were used in testing the hybrid system, and statistical significance measures were developed in analyzing the differences among different methods. When measured in terms of the number of secondary structures (rather than the number of residues) that were predicted correctly, the prediction produced by the hybrid system was also better than those of individual experts.

关键词： protein secondary structure prediction HYBRID SYSTEM NEURAL NETWORKS MEMORY-BASED REASONING STATISTICAL METHODS

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Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses

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JOURNAL OF MOLECULAR BIOLOGY 1996年第2期263卷 196-208页

作者： Goldman, N Thorne, JL Jones, DT N CAROLINA STATE UNIV DEPT STAT PROGRAM STAT GENET RALEIGH NC 27695 USA UNIV WARWICK DEPT BIOL SCI COVENTRY CV4 7AL W MIDLANDS ENGLAND

Previously proposed methods for protein secondary structure prediction from multiple sequence alignments do not efficiently extract the evolutionary information that these alignments contain. The predictions of these methods are less accurate than they could be, because of their failure to consider explicitly the phylogenetic tree that relates aligned protein sequences. As an alternative, we present a hidden Markov model approach to secondary structure prediction that more fully uses the evolutionary information contained in protein sequence alignments, A representative example is presented, and three experiments are performed that illustrate how the appropriate representation of evolutionary relatedness can improve inferences. We explain why similar improvement can be expected in other secondary structure prediction methods and indeed any comparative sequence analysis method, (C) 1996 Academic Press Limited

关键词： comparative sequence analysis evolution hidden Markov model phylogenetic trees protein secondary structure prediction

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Profiles and Majority Voting-Based Ensemble Method for protein secondary structure prediction

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EVOLUTIONARY BIOINFORMATICS 2011年第7期7卷 171-188页

作者： Bouziane, Hafida Messabih, Belhadri Chouarfia, Abdallah USTO MB Univ Dept Comp Sci Oran Algeria

Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted attention recently. Today, the main goal remains to improve the prediction quality of the secondary structure elements. The prediction accuracy has been continuously improved over the years, especially by using hybrid or ensemble methods and incorporating evolutionary information in the form of profiles extracted from alignments of multiple homologous sequences. In this paper, we investigate how best to combine k-NNs, ANNs and Multi-class SVMs (M-SVMs) to improve secondary structure prediction of globular proteins. An ensemble method which combines the outputs of two feed-forward ANNs, k-NN and three M-SVM classifiers has been applied. Ensemble members are combined using two variants of majority voting rule. An heuristic based filter has also been applied to refine the prediction. To investigate how much improvement the general ensemble method can give rather than the individual classifiers that make up the ensemble, we have experimented with the proposed system on the two widely used benchmark datasets RS126 and CB513 using cross-validation tests by including PSI-BLAST position-specific scoring matrix (PSSM) profiles as inputs. The experimental results reveal that the proposed system yields significant performance gains when compared with the best individual classifier.

关键词： protein secondary structure prediction k-Nearest Neighbors feed-forward Neural Networks Multi-class Support Vector Machines (M-SVMs) ensemble method Position-Specific Scoring Matrix (PSSM) profiles

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Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction

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JOURNAL OF MOLECULAR MODELING 2012年第9期18卷 4275-4289页

作者： Saraswathi, S. Fernandez-Martinez, J. L. Kolinski, A. Jernigan, R. L. Kloczkowski, A. Ohio State Univ Coll Med Battelle Ctr Math Med Res InstNationwide Childrens HospDept Pediat Columbus OH 43205 USA Univ Oviedo Dept Math Oviedo Spain Univ Warsaw Fac Chem Lab Theory Biopolymers PL-02093 Warsaw Poland Iowa State Univ Dept Biochem Biophys & Mol Biol Ames IA USA

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computational methods to predict structures and identify their functions from the sequence. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, including drug development and discovery of biomarkers. A novel method called fast learning optimized prediction methodology (FLOPRED) is proposed for predicting protein secondary structure, using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data that yield better and faster convergence to produce more accurate results. protein secondary structures are predicted reliably, more efficiently and more accurately using FLOPRED. These techniques yield superior classification of secondary structure elements, with a training accuracy ranging between 83 % and 87 % over a widerange of hidden neurons and a cross-validated testing accuracy ranging between 81 % and 84 % and a segment overlap (SOV) score of 78 % that are obtained with different sets of proteins. These results are comparable to other recently published studies, but are obtained with greater efficiencies, in terms of time and cost.

关键词： Knowledge-based potentials Machine learning Neural networks Particle swarm optimization protein secondary structure prediction

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